1. Field of the Invention
The present invention relates to an image processing method and, more particular, relates to an image processing method using a cellular simultaneous recurrent network.
2. Description of the Related Art
As is known, feed-forward neural networks are unable to process data with time dependent information and are thus impractical for image processing and handling data in typical images. Cellular neural networks have been used for performing image processing tasks and are capable of performing fractional and single pixel translation. However, cellular neural networks have shown limited success with geometric transformations and image registration. Further, known methods of cellular neural network imaging processing in the related art typically compute weights mathematically and not through a learning process. Methods that utilize back-propagation to train cellular neural networks to perform imaging tasks such as loss-less image coding and modeling mechanical vibration have been recently developed. However, the known methods of imaging processing using cellular neural networks are not capable of learning to perform image processing tasks such as geometric transformations.
Moreover, cellular simultaneous recurrent networks were developed for both long-term optimization and learning. These networks have been developed to show that neural networks may be applied to image optimization. For example, FIG. 1 shows a cellular structure of the cellular simultaneous recurrent network for image processing. As shown in FIG. 1, each cell structure includes a plurality of cells 100 which each contain a core network. Notably, there is a one-to-one correspondence between the input image 105, each cell 100, and the output image 110.